OpenRaiser/Intern-Atlas huggingface dataset:
An Overview of Low latency for Wireless Communications: an Evolutionary Perspective
Venue: arXiv cs.NI 2021
Year: 2021

Ultra-low latency supported by the fifth generation (5G) give impetus to the prosperity of many wireless network applications, such as autonomous driving, robotics, telepresence, virtual reality and so on. Ultra-low latency is not achieved in a moment, but requires long-term evolution of network structure and key enabling communication technologies. In this paper, we provide an evolutionary overview of low latency in mobile communication systems, including two different evolutionary perspectives: 1) network architecture; 2) physical layer air interface technologies. We firstly describe in detail the evolution of communication network architecture from the second generation (2G) to 5G, highlighting the key points reducing latency. Moreover, we review the evolution of key enabling technologies in the physical layer from 2G to 5G, which is also aimed at reducing latency. We also discussed the challenges and future research directions for low latency in network architecture and physical layer.
POSLAN: Disentangling Chat with Positional and Language encoded Post Embeddings
Venue: arXiv cs.CL 2021
Year: 2021

Most online message threads inherently will be cluttered and any new user or an existing user visiting after a hiatus will have a difficult time understanding whats being discussed in the thread. Similarly cluttered responses in a message thread makes analyzing the messages a difficult problem. The need for disentangling the clutter is much higher when the platform where the discussion is taking place does not provide functions to retrieve reply relations of the messages. This introduces an interesting problem to which \cite{wang2011learning} phrases as a structural learning problem. We create vector embeddings for posts in a thread so that it captures both linguistic and positional features in relation to a context of where a given message is in. Using these embeddings for posts we compute a similarity based connectivity matrix which then converted into a graph. After employing a pruning mechanisms the resultant graph can be used to discover the reply relation for the posts in the thread. The process of discovering or disentangling chat is kept as an unsupervised mechanism. We present our experimental results on a data set obtained from Telegram with limited meta data.
Dynamic Data-Race Detection through the Fine-Grained Lens
Venue: arXiv cs.PL 2021
Year: 2021

Data races are among the most common bugs in concurrency. The standard approach to data-race detection is via dynamic analyses, which work over executions of concurrent programs, instead of the program source code. The rich literature on the topic has created various notions of dynamic data races, which are known to be detected efficiently when certain parameters (e.g., number of threads) are small. However, the \emph{fine-grained} complexity of all these notions of races has remained elusive, making it impossible to characterize their trade-offs between precision and efficiency. In this work we establish several fine-grained separations between many popular notions of dynamic data races. The input is an execution trace with $N$ events, $T$ threads and $L$ locks. Our main results are as follows. First, we show that happens-before (HB) races can be detected in $O(N\cdot \min(T, L))$ time, improving over the standard $O(N\cdot T)$ bound when $L=o(T)$. Moreover, we show that even reporting an HB race that involves a read access is hard for 2-orthogonal vectors (2-OV). This is the first rigorous proof of the conjectured quadratic lower-bound in detecting HB races. Second, we show that the recently introduced synchronization-preserving races are hard to detect for OV-3 and thus have a cubic lower bound, when $T=Ω(N)$. This establishes a complexity separation from HB races which are known to be less expressive. Third, we show that lock-cover races are hard for 2-OV, and thus have a quadratic lower-bound, even when $T=2$ and $L = ω(\log N)$. The similar notion of lock-set races is known to be detectable in $O(N\cdot L)$ time, and thus we achieve a complexity separation between the two. Moreover, we show that lock-set races become hitting-set (HS)-hard when $L=Θ(N)$, and thus also have a quadratic lower bound, when the input is sufficiently complex.
A Note on General Statistics of Publicly Accessible Knowledge Bases
Venue: arXiv cs.DB 2021
Year: 2021

Knowledge bases are prevalent in various domains and have been widely used in a large number of real applications such as applications in online encyclopedia, social media, biomedical fields, bibliographical networks. Due to their great importance, knowledge bases have received much attention from both the academia and industry community in recent years. In this paper, we provide a summary of the general statistics of several open-source and publicly accessible knowledge bases, ranging from the number of objects, relations to the object types and the relation types. With such statistics, this concise note can not only help researchers form a better and quick understanding of existing open accessible knowledge bases, but can also guide the general audience to use the resource effectively when they conduct research with knowledge bases.
Rethinking of Pedestrian Attribute Recognition: A Reliable Evaluation under Zero-Shot Pedestrian Identity Setting
Venue: arXiv cs.CV 2021
Year: 2021

Pedestrian attribute recognition aims to assign multiple attributes to one pedestrian image captured by a video surveillance camera. Although numerous methods are proposed and make tremendous progress, we argue that it is time to step back and analyze the status quo of the area. We review and rethink the recent progress from three perspectives. First, given that there is no explicit and complete definition of pedestrian attribute recognition, we formally define and distinguish pedestrian attribute recognition from other similar tasks. Second, based on the proposed definition, we expose the limitations of the existing datasets, which violate the academic norm and are inconsistent with the essential requirement of practical industry application. Thus, we propose two datasets, PETA\textsubscript{$ZS$} and RAP\textsubscript{$ZS$}, constructed following the zero-shot settings on pedestrian identity. In addition, we also introduce several realistic criteria for future pedestrian attribute dataset construction. Finally, we reimplement existing state-of-the-art methods and introduce a strong baseline method to give reliable evaluations and fair comparisons. Experiments are conducted on four existing datasets and two proposed datasets to measure progress on pedestrian attribute recognition.
Validation and Inference of Agent Based Models
Venue: arXiv cs.AI 2021
Year: 2021

Agent Based Modelling (ABM) is a computational framework for simulating the behaviours and interactions of autonomous agents. As Agent Based Models are usually representative of complex systems, obtaining a likelihood function of the model parameters is nearly always intractable. There is a necessity to conduct inference in a likelihood free context in order to understand the model output. Approximate Bayesian Computation is a suitable approach for this inference. It can be applied to an Agent Based Model to both validate the simulation and infer a set of parameters to describe the model. Recent research in ABC has yielded increasingly efficient algorithms for calculating the approximate likelihood. These are investigated and compared using a pedestrian model in the Hamilton CBD.
Predicting Disease Progress with Imprecise Lab Test Results
Venue: arXiv cs.LG 2021
Year: 2021

In existing deep learning methods, almost all loss functions assume that sample data values used to be predicted are the only correct ones. This assumption does not hold for laboratory test data. Test results are often within tolerable or imprecision ranges, with all values in the ranges acceptable. By considering imprecision samples, we propose an imprecision range loss (IR loss) method and incorporate it into Long Short Term Memory (LSTM) model for disease progress prediction. In this method, each sample in imprecision range space has a certain probability to be the real value, participating in the loss calculation. The loss is defined as the integral of the error of each point in the impression range space. A sampling method for imprecision space is formulated. The continuous imprecision space is discretized, and a sequence of imprecise data sets are obtained, which is convenient for gradient descent learning. A heuristic learning algorithm is developed to learn the model parameters based on the imprecise data sets. Experimental results on real data show that the prediction method based on IR loss can provide more stable and consistent prediction result when test samples are generated from imprecision range.
Generalization Error of GAN from the Discriminator's Perspective
Venue: arXiv cs.LG 2021
Year: 2021

The generative adversarial network (GAN) is a well-known model for learning high-dimensional distributions, but the mechanism for its generalization ability is not understood. In particular, GAN is vulnerable to the memorization phenomenon, the eventual convergence to the empirical distribution. We consider a simplified GAN model with the generator replaced by a density, and analyze how the discriminator contributes to generalization. We show that with early stopping, the generalization error measured by Wasserstein metric escapes from the curse of dimensionality, despite that in the long term, memorization is inevitable. In addition, we present a hardness of learning result for WGAN.
ComFormer: Code Comment Generation via Transformer and Fusion Method-based Hybrid Code Representation
Venue: arXiv cs.SE 2021
Year: 2021

Developers often write low-quality code comments due to the lack of programming experience, which can reduce the efficiency of developers program comprehension. Therefore, developers hope that code comment generation tools can be developed to illustrate the functionality and purpose of the code. Recently, researchers mainly model this problem as the neural machine translation problem and tend to use deep learning-based methods. In this study, we propose a novel method ComFormer based on Transformer and fusion method-based hybrid code presentation. Moreover, to alleviate OOV (out-of-vocabulary) problem and speed up model training, we further utilize the Byte-BPE algorithm to split identifiers and Sim_SBT method to perform AST Traversal. We compare ComFormer with seven state-of-the-art baselines from code comment generation and neural machine translation domains. Comparison results show the competitiveness of ComFormer in terms of three performance measures. Moreover, we perform a human study to verify that ComFormer can generate high-quality comments.
On Submodular Prophet Inequalities and Correlation Gap
Venue: arXiv cs.DS 2021
Year: 2021

Prophet inequalities and secretary problems have been extensively studied in recent years due to their elegance, connections to online algorithms, stochastic optimization, and mechanism design problems in game theoretic settings. Rubinstein and Singla developed a notion of combinatorial prophet inequalities in order to generalize the standard prophet inequality setting to combinatorial valuation functions such as submodular and subadditive functions. For non-negative submodular functions they demonstrated a constant factor prophet inequality for matroid constraints. Along the way they showed a variant of the correlation gap for non-negative submodular functions. In this paper we revisit their notion of correlation gap as well as the standard notion of correlation gap and prove much tighter and cleaner bounds. Via these bounds and other insights we obtain substantially improved constant factor combinatorial prophet inequalities for both monotone and non-monotone submodular functions over any constraint that admits an Online Contention Resolution Scheme. In addition to improved bounds we describe efficient polynomial-time algorithms that achieve these bounds.
Graph and Recurrent Neural Network-based Vehicle Trajectory Prediction For Highway Driving
Venue: arXiv cs.RO 2021
Year: 2021

Integrating trajectory prediction to the decision-making and planning modules of modular autonomous driving systems is expected to improve the safety and efficiency of self-driving vehicles. However, a vehicle's future trajectory prediction is a challenging task since it is affected by the social interactive behaviors of neighboring vehicles, and the number of neighboring vehicles can vary in different situations. This work proposes a GNN-RNN based Encoder-Decoder network for interaction-aware trajectory prediction, where vehicles' dynamics features are extracted from their historical tracks using RNN, and the inter-vehicular interaction is represented by a directed graph and encoded using a GNN. The parallelism of GNN implies the proposed method's potential to predict multi-vehicular trajectories simultaneously. Evaluation on the dataset extracted from the NGSIM US-101 dataset shows that the proposed model is able to predict a target vehicle's trajectory in situations with a variable number of surrounding vehicles.
Towards Autonomous Pipeline Inspection with Hierarchical Reinforcement Learning
Venue: arXiv cs.RO 2021
Year: 2021

Inspection and maintenance are two crucial aspects of industrial pipeline plants. While robotics has made tremendous progress in the mechanic design of in-pipe inspection robots, the autonomous control of such robots is still a big open challenge due to the high number of actuators and the complex manoeuvres required. To address this problem, we investigate the usage of Deep Reinforcement Learning for achieving autonomous navigation of in-pipe robots in pipeline networks with complex topologies. Moreover, we introduce a hierarchical policy decomposition based on Hierarchical Reinforcement Learning to learn robust high-level navigation skills. We show that the hierarchical structure introduced in the policy is fundamental for solving the navigation task through pipes and necessary for achieving navigation performances superior to human-level control.
Proceedings of the First Workshop on Weakly Supervised Learning (WeaSuL)
Venue: arXiv cs.LG 2021
Year: 2021

Welcome to WeaSuL 2021, the First Workshop on Weakly Supervised Learning, co-located with ICLR 2021. In this workshop, we want to advance theory, methods and tools for allowing experts to express prior coded knowledge for automatic data annotations that can be used to train arbitrary deep neural networks for prediction. The ICLR 2021 Workshop on Weak Supervision aims at advancing methods that help modern machine-learning methods to generalize from knowledge provided by experts, in interaction with observable (unlabeled) data. In total, 15 papers were accepted. All the accepted contributions are listed in these Proceedings.
Bag of Tricks for Neural Architecture Search
Venue: arXiv cs.LG 2021
Year: 2021

While neural architecture search methods have been successful in previous years and led to new state-of-the-art performance on various problems, they have also been criticized for being unstable, being highly sensitive with respect to their hyperparameters, and often not performing better than random search. To shed some light on this issue, we discuss some practical considerations that help improve the stability, efficiency and overall performance.
The Three Ensemble Clustering (3EC) Algorithm for Pattern Discovery in Unsupervised Learning
Venue: arXiv cs.LG 2021
Year: 2021

This paper presents a multiple learner algorithm called the 'Three Ensemble Clustering 3EC' algorithm that classifies unlabeled data into quality clusters as a part of unsupervised learning. It offers the flexibility to explore the context of new clusters formed by an ensemble of algorithms based on internal validation indices. It is worth mentioning that the input data set is considered to be a cluster of clusters. An anomaly can possibly manifest as a cluster as well. Each partitioned cluster is considered to be a new data set and is a candidate to explore the most optimal algorithm and its number of partition splits until a predefined stopping criteria is met. The algorithms independently partition the data set into clusters and the quality of the partitioning is assessed by an ensemble of internal cluster validation indices. The 3EC algorithm presents the validation index scores from a choice of algorithms and its configuration of partitions and it is called the Tau Grid. 3EC chooses the most optimal score. The 3EC algorithm owes its name to the two input ensembles of algorithms and internal validation indices and an output ensemble of final clusters. Quality plays an important role in this clustering approach and it also acts as a stopping criteria from further partitioning. Quality is determined based on the quality of the clusters provided by an algorithm and its optimal number of splits. The 3EC algorithm determines this from the score of the ensemble of validation indices. The user can configure the stopping criteria by providing quality thresholds for the score range of each of the validation indices and the optimal size of the output cluster. The users can experiment with different sets of stopping criteria and choose the most 'sensible group' of quality clusters
The Satisfiability Problem for a Quantitative Fragment of PCTL
Venue: arXiv cs.LO 2021
Year: 2021

We give a sufficient condition under which every finite-satisfiable formula of a given PCTL fragment has a model with at most doubly exponential number of states (consequently, the finite satisfiability problem for the fragment is in 2-EXPSPACE). The condition is semantic and it is based on enforcing a form of ``progress'' in non-bottom SCCs contributing to the satisfaction of a given PCTL formula. We show that the condition is satisfied by PCTL fragments beyond the reach of existing methods.
COMBO: a new module for EUD parsing
Venue: arXiv cs.CL 2021
Year: 2021

We introduce the COMBO-based approach for EUD parsing and its implementation, which took part in the IWPT 2021 EUD shared task. The goal of this task is to parse raw texts in 17 languages into Enhanced Universal Dependencies (EUD). The proposed approach uses COMBO to predict UD trees and EUD graphs. These structures are then merged into the final EUD graphs. Some EUD edge labels are extended with case information using a single language-independent expansion rule. In the official evaluation, the solution ranked fourth, achieving an average ELAS of 83.79%. The source code is available at https://gitlab.clarin-pl.eu/syntactic-tools/combo.
SmartReviews: Towards Human- and Machine-actionable Reviews
Venue: arXiv cs.DL 2021
Year: 2021

Review articles summarize state-of-the-art work and provide a means to organize the growing number of scholarly publications. However, the current review method and publication mechanisms hinder the impact review articles can potentially have. Among other limitations, reviews only provide a snapshot of the current literature and are generally not readable by machines. In this work, we identify the weaknesses of the current review method. Afterwards, we present the SmartReview approach addressing those weaknesses. The approach pushes towards semantic community-maintained review articles. At the core of our approach, knowledge graphs are employed to make articles more machine-actionable and maintainable.
Causal affect prediction model using a facial image sequence
Venue: arXiv cs.CV 2021
Year: 2021

Among human affective behavior research, facial expression recognition research is improving in performance along with the development of deep learning. However, for improved performance, not only past images but also future images should be used along with corresponding facial images, but there are obstacles to the application of this technique to real-time environments. In this paper, we propose the causal affect prediction network (CAPNet), which uses only past facial images to predict corresponding affective valence and arousal. We train CAPNet to learn causal inference between past images and corresponding affective valence and arousal through supervised learning by pairing the sequence of past images with the current label using the Aff-Wild2 dataset. We show through experiments that the well-trained CAPNet outperforms the baseline of the second challenge of the Affective Behavior Analysis in-the-wild (ABAW2) Competition by predicting affective valence and arousal only with past facial images one-third of a second earlier. Therefore, in real-time application, CAPNet can reliably predict affective valence and arousal only with past data.
Social Media Marketing (SMM) A Strategic Tool for Developing Business for Tourism Companies
Venue: arXiv cs.CY 2021
Year: 2021

Social media marketing is an emerging marketing technique worldwide. This research concentrates on how effectively social media can be used to promote a product in tourism industry. The efficient use of social media develops a tourism company in terms of sales, branding, reach and relationship management. The study aims to find the best social media platform to promote and develop a tourism company and the customer opinion towards planning a trip through online. It also concentrates on customer response for online offers and discounts in those social media platforms. The study attempts to understand and create suitable model for social media marketing for tourism companies with a sample size of 400. The sampling technique used in this study is purposive sampling method. The purposive sample can also be called as judgemental sample. Normally the sample will be selected based on the knowledge possessed by the respondents on a particular phenomenon. Here, the study is been conducted among the people who use social media. The sampling technique helped the researcher to identify the target sample i.e., the social media users.
Lower Bounds for the Query Complexity of Equilibria in Lipschitz Games
Venue: arXiv cs.GT 2021
Year: 2021

Nearly a decade ago, Azrieli and Shmaya introduced the class of $λ$-Lipschitz games in which every player's payoff function is $λ$-Lipschitz with respect to the actions of the other players. They showed that such games admit $ε$-approximate pure Nash equilibria for certain settings of $ε$ and $λ$. They left open, however, the question of how hard it is to find such an equilibrium. In this work, we develop a query-efficient reduction from more general games to Lipschitz games. We use this reduction to show a query lower bound for any randomized algorithm finding $ε$-approximate pure Nash equilibria of $n$-player, binary-action, $λ$-Lipschitz games that is exponential in $\frac{nλ}ε$. In addition, we introduce ``Multi-Lipschitz games,'' a generalization involving player-specific Lipschitz values, and provide a reduction from finding equilibria of these games to finding equilibria of Lipschitz games, showing that the value of interest is the sum of the individual Lipschitz parameters. Finally, we provide an exponential lower bound on the deterministic query complexity of finding $ε$-approximate correlated equilibria of $n$-player, $m$-action, $λ$-Lipschitz games for strong values of $ε$, motivating the consideration of explicitly randomized algorithms in the above results. Our proof is arguably simpler than those previously used to show similar results.